Efficient fluorescence/phosphorescence hybrid white organic light-emitting diodes (OLEDs) with single doped co-host structure have been fabricated. Device using 9-Naphthyl-10 -(4-triphenylamine)anthrancene as the fluorescent dopant and Ir(ppy)3 and Ir(2-phq)3 as the green and orange phosphorescent dopants show the luminous efficiency of 12.4% (17.6 lm/W, 27.5 cd/A) at 1000 cd/m2. Most important to note that the efficiency-brightness roll-off of the device was very mild. With the brightness rising up to 5000 and 10 000 cd/m2, the efficiency could be kept at 11.8% (14.0 lm/W, 26.5 cd/A) and 11.0% (11.8 lm/W, 25.0 cd/A). The Commission Internationale de L'Eclairage (CIE) coordinates and color rending index (CRI) were measured to be (0.45, 0.48) and 65, respectively, and remained the same in a large range of brightness (1000–10 000 cd/m2), which is scarce in the reported white OLEDs. The performance of the device at high luminance (5000 and 10 000 cd/m2) was among the best reported results including fluorescence/phosphorescence hybrid and all-phosphorescent white OLEDs. Moreover, the CRI of the white OLED can be improved to 83 by using a yellow-green emitter (Ir(ppy)2bop) in the device.

Insulator string is a special insulation component which plays an important role in overhead transmission lines. However, working outdoors for a long time, insulators often have defects because of various environmental and weather conditions, which affect the normal operation of transmission lines and even cause huge economic losses. Therefore, insulator defect recognition is a crucial issue. Traditional insulator defect identification relies on manual work, which is time-consuming and inefficient. Therefore, the use of artificial intelligence to detect the defect location and recognize its class has become a key research field. By improving the classical YOLOv5 (you only look once) model, this article proposes a new method to enable high accuracy and real-time detection. Our method has three advantages: 1) Efficient-IoU (EIoU) replaces intersection over union (IoU) to calculate the loss of box regression, which overcomes that the detection is sensitive to various scale insulators in aerial images. 2) Since YOLOv5 itself detects some natural scenes in the real world, some anchors setting by default are not suitable for defect detection, this article introduces Assumption-free K-MC2 (AFK-MC2) algorithm into YOLOv5 to modify the K-means algorithm to improve accuracy and speed. 3) The cluster non-maximum suppression (Cluster-NMS) algorithm is introduced to avoid missing detection of insulators because of mutual occlusion in images and improve the computation speed at the same time. The experiments’ results show that this model can improve detection accuracy compared with YOLOv5 and realize real-time detection.

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